Search

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. [ABSTRACT FROM AUTHOR]

BACKGROUND: The effect of cancer on employment and retirement status in an older work force is not well understood. This study examines whether cancer survivors were less likely to be working than a sibling comparison group. OBJECTIVES: To compare work-related variables between older cancer survivors and a group of non-cancer sibling controls. A secondary objective was to evaluate the effect of cancer site and time since cancer diagnosis on work-related variables. METHODS: Data from the Wisconsin Longitudinal Study (WLS) were used to assess work outcomes in cancer survivors (+CA, n=539, mean age=65.81, SD=4.75 years) and non-cancer sibling controls (-CA, n=539, mean age=63.95, SD=5.31 years). RESULTS: Survivors (+CA group) were more likely to report not working (61.8%) and to be completely retired (55%) than the -CA group (48.3% not employed; 42% retired). Controlling for age, gender and education, this effect persisted with the +CA group more likely to be not working (OR=1.40; 95% CI=1.08 to 1.83) and completely retired (OR=1.36; 95% CI=1.05 to 1.77) than the -CA group. Neither time since diagnosis nor cancer site affected work outcomes. CONCLUSIONS: In this study, older +CA survivors were less likely to be working and more likely to be completely retired than -CA sibling controls. Future research should evaluate factors affecting work status among older cancer survivors. [ABSTRACT FROM AUTHOR]

The tumor-node-metastasis staging system has been the lynchpin of cancer diagnosis, treatment, and prognosis for many years. For meaningful clinical use, an orderly grouping of the T and N categories into a staging system needs to be defined, usually with respect to a time-to-event outcome. This can be reframed as a model selection problem with respect to features arranged on a partially ordered two-way grid, and a penalized regression method is proposed for selecting the optimal grouping. Instead of penalizing the L1-norm of the coefficients like lasso, in order to enforce the stage grouping, we place L1 constraints on the differences between neighboring coefficients. The underlying mechanism is the sparsity-enforcing property of the L1 penalty, which forces some estimated coefficients to be the same and hence leads to stage grouping. Partial ordering constraints is also required as both the T and N categories are ordinal. A series of optimal groupings with different numbers of stages can be obtained by varying the tuning parameter, which gives a tree-like structure offering a visual aid on how the groupings are progressively made. We hence call the proposed method the lasso tree. We illustrate the utility of our method by applying it to the staging of colorectal cancer using survival outcomes. Simulation studies are carried out to examine the finite sample performance of the selection procedure. We demonstrate that the lasso tree is able to give the right grouping with moderate sample size, is stable with regard to changes in the data, and is not affected by random censoring. [ABSTRACT FROM PUBLISHER]

Purpose: We sought to describe clinical and psychosocial characteristics of overweight women with asthma. Methods: Telephone interview and medical record review involving 808 women with asthma participating in a randomized study to identify those who were overweight. We assessed the relationship of their weight to asthma symptoms, health care use, quality of life, self-esteem, need for social support, and demographic characteristics. Regression analyses were used to investigate relationships between overweight and asthma. Findings: Sixty-eight percent of the women in the study were overweight or obese. Demographic characteristics associated with overweight in women with asthma included being minority (p =.000), having a lower education level (p =.000), and a lower household income (p =.024). Overweight was associated with greater health care use, comorbidities (acid reflux, urinary incontinence), and persistent disease (p =.001). Overweight women exhibited lower self-esteem (p =.002) and lower perceived quality of life (p =.000). Conclusion: Overweight females with asthma experience significant challenges because of their weight, more persistent and severe disease, specific comorbidities, and lower rates of obtaining psychosocial resources. Clinical consultations and interventions should account for the influence of overweight on asthma control and health status in female patients. [Copyright &y& Elsevier]